Colon/Rectal Adenocarcinoma: Correlation between gene methylation status and clinical features
(primary solid tumor cohort)
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 17147 genes and 12 clinical features across 239 samples, statistically thresholded by Q value < 0.05, 10 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • GDNF

  • 916 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.

    • PAK2 ,  AZI2 ,  TEF ,  GATAD2B ,  HIST1H4C ,  ...

  • 7 genes correlated to 'GENDER'.

    • KIF4B ,  GPX1 ,  POLDIP3 ,  RIMBP3 ,  PAFAH1B2 ,  ...

  • 173 genes correlated to 'HISTOLOGICAL.TYPE'.

    • METTL3 ,  ZNF668 ,  GPR137B ,  ZNRF1 ,  C20ORF43 ,  ...

  • 6 genes correlated to 'PATHOLOGY.N'.

    • UBE2L6 ,  CASP1 ,  CASP5 ,  SP140L ,  SP100 ,  ...

  • 241 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • JOSD2 ,  FAM86B2 ,  ETV5 ,  DMKN ,  TMOD2 ,  ...

  • 2 genes correlated to 'TUMOR.STAGE'.

    • UBE2L6 ,  SP140L

  • 207 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • SFRS13B ,  ZNF599 ,  MDFIC ,  ZNF665 ,  FLJ45983 ,  ...

  • 125 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • GGCT ,  ZNF235 ,  SLC2A11 ,  SGSM3 ,  SUSD2 ,  ...

  • 2 genes correlated to 'NUMBER.OF.LYMPH.NODES'.

    • CASP1 ,  UBE2L6

  • No genes correlated to 'Time to Death', and 'PATHOLOGY.T'.

Results
Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1.  Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=1 older N=1 younger N=0
PRIMARY SITE OF DISEASE t test N=916 rectum N=121 colon N=795
GENDER t test N=7 male N=1 female N=6
HISTOLOGICAL TYPE ANOVA test N=173        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test N=6 higher pN N=6 lower pN N=0
PATHOLOGICSPREAD(M) ANOVA test N=241        
TUMOR STAGE Spearman correlation test N=2 higher stage N=2 lower stage N=0
RADIATIONS RADIATION REGIMENINDICATION t test N=207 yes N=197 no N=10
COMPLETENESS OF RESECTION ANOVA test N=125        
NUMBER OF LYMPH NODES Spearman correlation test N=2 higher number.of.lymph.nodes N=2 lower number.of.lymph.nodes N=0
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0.1-129.1 (median=6)
  censored N = 194
  death N = 26
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One gene related to 'AGE'.

Table S2.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 64.79 (13)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
GDNF 0.308 1.268e-06 0.0217

Figure S1.  Get High-res Image As an example, this figure shows the association of GDNF to 'AGE'. P value = 1.27e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

916 genes related to 'PRIMARY.SITE.OF.DISEASE'.

Table S4.  Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE Labels N
  COLON 189
  RECTUM 48
     
  Significant markers N = 916
  Higher in RECTUM 121
  Higher in COLON 795
List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

T(pos if higher in 'RECTUM') ttestP Q AUC
PAK2 -10.94 5.941e-22 1.02e-17 0.8457
AZI2 -10.59 4.863e-21 8.34e-17 0.8636
TEF -10.26 3.051e-20 5.23e-16 0.8398
GATAD2B -10.13 8.474e-20 1.45e-15 0.8659
HIST1H4C -9.78 4.123e-19 7.07e-15 0.8498
C2CD2 -9.67 8.062e-19 1.38e-14 0.8262
MAN2A2 -9.38 1.232e-17 2.11e-13 0.8268
THOP1 9.25 1.562e-17 2.68e-13 0.8941
FAM13C -9.18 5.486e-17 9.4e-13 0.914
RAB22A -9.02 6.718e-17 1.15e-12 0.8465

Figure S2.  Get High-res Image As an example, this figure shows the association of PAK2 to 'PRIMARY.SITE.OF.DISEASE'. P value = 5.94e-22 with T-test analysis.

Clinical variable #4: 'GENDER'

7 genes related to 'GENDER'.

Table S6.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 112
  MALE 127
     
  Significant markers N = 7
  Higher in MALE 1
  Higher in FEMALE 6
List of 7 genes differentially expressed by 'GENDER'

Table S7.  Get Full Table List of 7 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
KIF4B -10.69 6.161e-22 1.06e-17 0.8352
GPX1 -9.21 1.754e-17 3.01e-13 0.8103
POLDIP3 -8.58 2.143e-15 3.67e-11 0.7942
RIMBP3 6.45 7.345e-10 1.26e-05 0.7229
PAFAH1B2 -6.4 8.079e-10 1.38e-05 0.725
UBAP2 -6.01 7.08e-09 0.000121 0.6988
ZNF839 -4.9 1.786e-06 0.0306 0.6777

Figure S3.  Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 6.16e-22 with T-test analysis.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

173 genes related to 'HISTOLOGICAL.TYPE'.

Table S8.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  COLON ADENOCARCINOMA 165
  COLON MUCINOUS ADENOCARCINOMA 24
  RECTAL ADENOCARCINOMA 46
  RECTAL MUCINOUS ADENOCARCINOMA 2
     
  Significant markers N = 173
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
METTL3 1.495e-25 2.56e-21
ZNF668 6.775e-24 1.16e-19
GPR137B 1.31e-21 2.25e-17
ZNRF1 4.595e-20 7.88e-16
C20ORF43 2.428e-18 4.16e-14
CATSPER2 5.481e-18 9.4e-14
EEF1DP3 7.146e-17 1.22e-12
C18ORF10 1.796e-16 3.08e-12
KIAA1328 1.796e-16 3.08e-12
ATP9B 4.168e-16 7.14e-12

Figure S4.  Get High-res Image As an example, this figure shows the association of METTL3 to 'HISTOLOGICAL.TYPE'. P value = 1.5e-25 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 2.89 (0.69)
  N
  T0 1
  T1 8
  T2 41
  T3 155
  T4 34
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGY.N'

6 genes related to 'PATHOLOGY.N'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.57 (0.77)
  N
  N0 141
  N1 55
  N2 40
     
  Significant markers N = 6
  pos. correlated 6
  neg. correlated 0
List of 6 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S12.  Get Full Table List of 6 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

SpearmanCorr corrP Q
UBE2L6 0.3797 1.646e-09 2.82e-05
CASP1 0.3725 3.493e-09 5.99e-05
CASP5 0.3515 2.868e-08 0.000492
SP140L 0.3396 8.831e-08 0.00151
SP100 0.3006 2.567e-06 0.044
CARD16 0.2999 2.719e-06 0.0466

Figure S5.  Get High-res Image As an example, this figure shows the association of UBE2L6 to 'PATHOLOGY.N'. P value = 1.65e-09 with Spearman correlation analysis.

Clinical variable #8: 'PATHOLOGICSPREAD(M)'

241 genes related to 'PATHOLOGICSPREAD(M)'.

Table S13.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 163
  M1 23
  M1A 6
  M1B 1
  MX 42
     
  Significant markers N = 241
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S14.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
JOSD2 5.157e-191 8.84e-187
FAM86B2 3.195e-146 5.48e-142
ETV5 1.581e-103 2.71e-99
DMKN 5.976e-69 1.02e-64
TMOD2 1.314e-67 2.25e-63
KCNK4 2.616e-66 4.48e-62
FZD3 2.527e-61 4.33e-57
TUBG2 4.728e-50 8.1e-46
MTUS2 6.583e-47 1.13e-42
FAHD2B 2.962e-41 5.08e-37

Figure S6.  Get High-res Image As an example, this figure shows the association of JOSD2 to 'PATHOLOGICSPREAD(M)'. P value = 5.16e-191 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

2 genes related to 'TUMOR.STAGE'.

Table S15.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 2.38 (0.94)
  N
  Stage 1 41
  Stage 2 90
  Stage 3 64
  Stage 4 32
     
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S16.  Get Full Table List of 2 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
UBE2L6 0.3152 1.258e-06 0.0216
SP140L 0.3095 1.981e-06 0.034

Figure S7.  Get High-res Image As an example, this figure shows the association of UBE2L6 to 'TUMOR.STAGE'. P value = 1.26e-06 with Spearman correlation analysis.

Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

207 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S17.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 5
  YES 234
     
  Significant markers N = 207
  Higher in YES 197
  Higher in NO 10
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S18.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
SFRS13B 12.69 1.729e-28 2.97e-24 0.7496
ZNF599 12.35 1.251e-24 2.14e-20 0.7564
MDFIC 11.73 1.493e-24 2.56e-20 0.7427
ZNF665 11.29 1.219e-23 2.09e-19 0.6513
FLJ45983 11.09 4.394e-23 7.53e-19 0.8376
OBSL1 12.68 1.375e-22 2.36e-18 0.7291
GPRC5B 12.08 1.646e-22 2.82e-18 0.8325
LRGUK 10.54 1.614e-21 2.77e-17 0.8446
ZNF737 10.44 4.812e-21 8.25e-17 0.7487
BATF3 10.7 4.685e-20 8.03e-16 0.7778

Figure S8.  Get High-res Image As an example, this figure shows the association of SFRS13B to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.73e-28 with T-test analysis.

Clinical variable #11: 'COMPLETENESS.OF.RESECTION'

125 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S19.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 148
  R1 1
  R2 2
  RX 24
     
  Significant markers N = 125
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S20.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
GGCT 4.076e-65 6.99e-61
ZNF235 3.528e-46 6.05e-42
SLC2A11 3.987e-25 6.84e-21
SGSM3 7.338e-24 1.26e-19
SUSD2 7.799e-24 1.34e-19
EIF5A 8.324e-24 1.43e-19
IDH3B 4.24e-23 7.27e-19
FER 4.377e-22 7.5e-18
YTHDF1 5.525e-22 9.47e-18
WDR46 1.362e-21 2.33e-17

Figure S9.  Get High-res Image As an example, this figure shows the association of GGCT to 'COMPLETENESS.OF.RESECTION'. P value = 4.08e-65 with ANOVA analysis.

Clinical variable #12: 'NUMBER.OF.LYMPH.NODES'

2 genes related to 'NUMBER.OF.LYMPH.NODES'.

Table S21.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.28 (5.2)
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S22.  Get Full Table List of 2 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
CASP1 0.3956 3.323e-09 5.7e-05
UBE2L6 0.3835 1.077e-08 0.000185

Figure S10.  Get High-res Image As an example, this figure shows the association of CASP1 to 'NUMBER.OF.LYMPH.NODES'. P value = 3.32e-09 with Spearman correlation analysis. The straight line presents the best linear regression.

Methods & Data
Input

  • Expresson data file = COADREAD-TP.meth.for_correlation.filtered_data.txt

  • Clinical data file = COADREAD-TP.clin.merged.picked.txt

  • Number of patients = 239

  • Number of genes = 17147

  • Number of clinical features = 12

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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